Paper: arXiv 2303.04688

Authors: Yanci Zhang, Mengjia Xia, Mingyang Li, Haitao Mao, Yutong Lu, Yupeng Lan, Jinlin Ye, Rui Dai

Abstract

Form 10-K report is a financial report disclosing the annual financial state of a public company. It is an important evidence to conduct financial analysis, i.e., asset pricing, corporate finance. Practitioners and researchers are constantly designing algorithms to better conduct analysis on information in the Form 10-K report. The vast majority of previous works focus on quantitative data. With recent advancement on natural language processing (NLP), textual data in financial filing attracts more attention. However, to incorporate textual data for analyzing, Form 10-K Itemization is a necessary pre-process step. It aims to segment the whole document into several Item sections, where each Item section focuses on a specific financial aspect of the company. With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks. In this paper, we develop a Form 10-K Itemization system which can automatically segment all the Item sections in 10-K documents. The system is both effective and efficient. It reaches a retrieval rate of 93%.

Complexity vs Empirical Score

  • Math Complexity: 1.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper addresses a practical text-processing problem in financial NLP, focusing on parsing SEC Form 10-Ks with an empirical retrieval rate evaluation (93%). Math complexity is minimal as it is an engineering and preprocessing pipeline, but it offers high practical utility for downstream textual analysis.

Research Flowchart

  flowchart TD
    A[Research Goal: Automate Form 10-K Document Itemization] --> B[Input Data: Full Form 10-K Annual Financial Reports]
    B --> C[Methodology: Automated Document Segmentation System]
    C --> D[Computational Process: Parse & Segment Text into Item Sections]
    D --> E[Outcome: Structured Item Sections for Downstream Financial NLP Tasks]
    E --> F[Key Finding: High Efficiency with 93% Retrieval Rate]